Estimating and Forecasting with a Dynamic Spatial Panel Data Model*. (9th January 2013)

• Record Type: Journal Article
• Authors: Baltagi, Badi H.; Fingleton, Bernard; Pirotte, Alain
• Is Part Of: Oxford bulletin of economics and statistics. Volume 76:Number 1(2014:Feb.)
• Page Start: 112
• Page End: 138
• Publication Date: 2013-01-09
• Subjects: Economic history -- Periodicals; Great Britain -- Economic conditions -- Periodicals; 330.9
• DOI: 10.1111/obes.12011
• Languages: English
• ISSNs: 0305-9049

Abstract

<abstract abstract-type="main" xml:lang="en"> <title>Abstract</title> <p>This study focuses on the estimation and predictive performance of several estimators for the dynamic and autoregressive spatial lag panel data model with spatially correlated disturbances. In the spirit of <xref ref-type="link" rid="b5">Arellano and Bond (1991)</xref> and <xref ref-type="link" rid="b35">Mutl (2006)</xref>, a dynamic spatial generalized method of moments (GMM) estimator is proposed based on <xref ref-type="link" rid="b25">Kapoor, Kelejian and Prucha (2007)</xref> for the spatial autoregressive (SAR) error model. The main idea is to mix non‐spatial and spatial instruments to obtain consistent estimates of the parameters. Then, a linear predictor of this spatial dynamic model is derived. Using Monte Carlo simulations, we compare the performance of the GMM spatial estimator to that of spatial and non‐spatial estimators and illustrate our approach with an application to new economic geography.</p> </abstract>